Analytics is becoming a crucial element in the enterprise data ecosystem. It is one of the key drivers of the Internet of Things (IoT), and will undoubtedly provide key competitive advantages as the digital economy unfolds.
But it doesn’t come cheap, and it is by no means an easy process to master. So as the enterprise finds itself between the rock of an increasingly data-driven business model and the hard place of having to create a highly sophisticated analytics environment, it is understandable that many organizations are willing to launch this particular endeavor on the cloud.
According to the Harvard Business Review, nearly 70 percent of organizations expect to have cloud-based analytics solutions up and running by the end of the year. The reasons vary from improved decision-making and forecasting to greater speed and efficiency, but underneath the operational benefits is a simple fact: The cloud offers the means to launch analytics infrastructure quickly and at the scale required of modern production environments. To be sure, issues like data migration and lack of customization exist in the cloud, but these are generally seen as secondary considerations to the need to put analytics to work quickly before business models are disrupted by a more nimble, data-savvy competitor.
IT platform providers who are transitioning from traditional licensing models to cloud-based subscription services are quickly ramping up their analytics capabilities for enterprises that lack the resources to build their own. Microsoft recently added new edge analytics streaming services and a centralized analysis engine to its Azure IoT Suite, providing a fully managed solution to start reaping the benefits of IoT data. The Azure Stream Analytics solution pushes analytics to IoT devices to reduce traffic to centralized resources, while the Time Series Insights tool utilizes Azure’s own analytics prowess to visualize time-stamped data to spot patterns and anomalies.
Another key function that is making its way to the cloud is voice recognition and analysis. Amazon recently added VoiceBase to its Connect contact center offering. The aim is to provide organizations with the means to analyze recorded conversations with customers to glean insights regarding service fulfillment, call tracking, workforce management and other tasks. The system integrates with the S3 storage platform and processing engines like Hadoop and Kinesis, with reports delivered through Amazon QuickSight, Qlik or Tableau.
And in somewhat of an ironic twist, cloud-based analytics engines can also be directed at optimizing the consumption and integration of cloud resources themselves. SnapLogic recently added a technology called Iris to its PaaS-based Enterprise Integration Cloud, which allows organizations to automate many of the cloud-provisioning tasks that are currently performed manually. The system crunches billions of metadata elements and other data points to provide step-by-step guidance for supporting data, applications and processes in the cloud, effectively providing line-of-business managers with a self-driving cloud that delivers desired results without the up-front complexity of cloud management.
While the cloud may provide an effective means of building and maintaining analytics infrastructure, it is still up to the enterprise to ensure the quality of the results it generates. The best way to do this is to run through processes, goals and business models with a fine-toothed comb to determine what you need to know and where to obtain the data that drives informed decisions.
As with any tool in the toolbox, the value of analytics is not based on how well it’s made, but how well it’s used.
Arthur Cole writes about infrastructure for IT Business Edge. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly. His contributions have appeared in Communications Today and Enterprise Networking Planet and as web content for numerous high-tech clients like TwinStrata and Carpathia. Follow Art on Twitter @acole602.
